Method and system of monitoring cardiac function based on patient position

ABSTRACT

A method of monitoring the cardiac function of a patient includes receiving a position input from a position sensor attached to the patient and classifying the position input into a position class. Cardiac waveform data is received for the patient and then the cardiac waveform data is compared to model waveform parameters for the position class.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/041,221, filed Feb. 11, 2016, which is incorporated herein byreference in entirety.

BACKGROUND

Cardiac waveforms, such as those measured by electrocardiograph (ECG)monitors, vary with body position changes by the patient. Such variationin cardiac waveforms due to changes in patient position are a commonsource of problems in cardiac monitoring, including triggering falsealarms. For example, changes in a patient's position may cause changesin the QRS and/or ST-T waveforms that are significant enough to triggera false alarm by an ECG monitor.

SUMMARY

The present disclosure generally relates to a method and system ofmonitoring cardiac function based on patient position.

One embodiment of a method of monitoring the cardiac function of apatient includes receiving a position input from a position sensorattached to the patient and classifying the position input into aposition class. Cardiac waveform data is received for the patient andthen the cardiac waveform data is compared to model waveform parametersfor the position class.

One embodiment of a system for monitoring cardiac function of a patientincludes an electrocardiograph monitor configured to record cardiacwaveform data from the patient and at least one position sensorconfigured to sense a position of the patient and produce a positioninput. The system further includes a processor, a position analysismodule, and a waveform analysis module. The position analysis module isexecutable on the processor to receive the position input, determine aposition class, wherein the position class is one of a predefined set ofposition classes, and output the position class. The waveform analysismodule is executable on the processor to receive the cardiac waveformdata and the position class, compare the cardiac waveform data to modelwaveform parameters for the position class to determine a cardiac statusof the patient, and output the cardiac status.

One embodiment of a non-transitory computer-readable medium havingcomputer executable instructions stored thereon has instructionsincluding the steps of receiving a position input from a position sensorattached to the patient, classifying the position input into a positionclass, wherein the position class is one of a predefined set of positionclasses, receiving cardiac waveform data for the patient, and comparingthe cardiac waveform data to model waveform parameters for the positionclass.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate the best mode presently contemplated of carryingout the disclosure. In the drawings:

FIG. 1 is a schematic diagram of one embodiment of a system formonitoring cardiac function of a patient.

FIGS. 2A-2D depict various patient positions exemplifying positionclasses.

FIGS. 3A and 3B graphically depict various position classifications.

FIG. 4 is a schematic diagram of one embodiment of a computing systemfor a system for monitoring cardiac function of a patient.

FIG. 5 depicts one embodiment of a method of monitoring cardiac functionof a patient.

FIG. 6 depicts another embodiment of a method of monitoring cardiacfunction of a patient.

DETAILED DESCRIPTION

The present disclosure was developed in view of the inventor'srecognition of the problems posed by changes in cardiac waveformsresulting from changes in patient position. Through experimentation andresearch in the relevant field, the present inventor recognized that bytracking patient position, current QRS, ST-T, and other ECG parametervalues can be compared to those recorded earlier when the patient was inthe same position, instead of comparing the current cardiac waveformvalues to values measured when the patient was in a different position.The cardiac waveforms may be associated with a position class based onpatient position measured by a position sensor at the time of thewaveform recording. Model waveform parameters may be established foreach position class based on the cardiac waveform data recorded from thepatient while the patient was in a position that falls within thatposition class. The model waveform parameters for that position classmay then be used as a baseline for comparison to subsequently recordedcardiac waveform data measured from the patient when the patient returnsto a position in that position class.

FIG. 1 depicts one embodiment of a system 1 of monitoring cardiacfunction of a patient. The system 1 includes an electrocardiograph (seeECG) monitor 8 that records cardiac waveform data 32 (also see FIG. 4)from the patient 4 via electrodes 6. The ECG monitor 8 may be any typeof electrocardiograph monitor, which may be a traditional wired ECGmonitor or a wireless ECG monitor. The system 1 for monitoring cardiacfunction of the patient 4 further includes two position sensors 10 a and10 b attached to the torso of the patient 4. The ECG monitor 8 and theposition sensors 10 a and 10 b communicate with the computing system200, and specifically with the processor 206 of the computing system200. The computing system 200 also includes a position analysis module52 executable on the processor 206 to receive position input informationfrom the position sensors 10 a and 10 b and determine a position classbased on the position input from the position sensors 10 a and 10 b. Thecomputing system 200 further includes a waveform analysis module 54 thatis executable on the processor 206 to receive the cardiac waveform data32 from the ECG monitor 8 and compare the cardiac waveform data 32 tomodel waveform parameters for the position class determined by theposition analysis module 52 to determine a cardiac status of thepatient. For example, the cardiac status may provide a classification ofthe cardiac waveform data 32 recorded from the patient on a continuumbetween healthy and a critical alarm status, according to criteria knownand available in the art for classifying cardia condition based onrecorded ECG waveforms. The waveform analysis module 54 may further beexecutable on the processor 206 in order to access and store informationin an ECG database 20. For example, the waveform analysis module 54 maybe executable to determine whether model waveform parameters exists fora particular position class identified by the position analysis module52. If such model waveform parameters do not exist for a particularposition class, then the waveform analysis module 54 may establish newmodel waveform parameters for that position class, as is explainedfurther herein. The model waveform parameters established by thewaveform analysis module 54 may be stored in ECG database 20 andaccessed any time that the patient 4 enters the relevant position class.Other information and output developed by the waveform analysis module54 executed on the processor 206 may also be stored in the ECG database20, including the cardiac status of the patient Likewise, the positionanalysis module 52 may be executable on the processor 206 to accessinformation stored in the ECG database 20 and/or may be executable tostore the determined position class in the ECG database 20. In otherembodiments, the model waveform parameters for the position classes maybe stored in local memory dedicated and/or accessible to the processor206 executing the position analysis module 52 and waveform analysismodule 54.

FIGS. 2A-2D depict various patient positions that may exemplify variouspredefined position classes. In the Figures, the patient 4 has aposition sensor 10 on their torso, and specifically placed in themidsection of the patient's chest approximately at the location of theheart. In other embodiments, the position sensor 10 may be placed atanother location on the patient's chest, such as higher up on thepatient's chest, on the patient's abdomen, on the patient's side, etc.In still other embodiments, two or more position sensors may beutilized, an example of which is depicted in FIG. 1. When more than oneposition sensor 10 is utilized, multiple position sensors may be placedon the patient's torso, such as to provide better ability to eliminatemeasurement results that do not indicate the orientation accurately fromany one position sensor. For example, accelerometers are often prone todetect the acceleration caused by a moving patient, such as changingposition or walking, for example, and these accelerations hide theorientation information of the sensor. Multiple position sensors may beutilized to eliminate the ambiguity of the position caused by theaccelerations created through patient movement. For example, the outputof each of the multiple position sensors may be compared to findcommonalities in sensed position, increasing the likelihood that theoutput is a measurement of patient position rather than noise. Forexample, when having an accelerometer positioned to the right-hand sideof the patient's chest, if the patient moves his right arm whileconducting an activity, such as eating, this sensor is likely to catchthe accelerations caused by the hand movement, and the patientorientation becomes ambiguous. In there is another accelerometerattached to the left-hand side of the patient's chest, thisaccelerometer is less prone to the movement accelerations, and thepatient position can be detected by using the signal of the secondaccelerometer. Further, the problem of such movement “artefacts” orambiguity may be solved by using a combined gyroscope-accelerometer asthe orientation sensor, which allows movement tracking even duringmotion.

In other embodiments were multiple position sensors are present, one ormore of the sensors may be placed on areas of the patient other than thepatient's torso, such as on the patient's arms or legs. This can providedetailed position information that may be used to define very preciseposition classes, and thus provide very precise waveform data modeling,where model waveform parameters are stored for each of the preciseposition classes. For example, the placement of a patient's arms when apatient is laying supine, or when lying on their side, may impact theECG recorded from the patient. Accordingly, in some embodiments it maybe desirable to place a position sensor 10 on each of the patient's armsso that arm position can be accounted for in the position classes. Themotion sensor(s) 10 may be attached to the patient by any suitablemeans, such as attached to the patient 4 by any suitable means, such asattached to the patient's skin by tape or pressed against the patient'sskin by a band or by form-fitting clothing.

The position sensor(s) 10 may be any sensor capable of providing anoutput relevant to motion and/or position of the patient. In oneembodiment, the position sensor 10 is an accelerometer, such as athree-axis accelerometer. In other embodiments, the position sensor(s)10 may be a gyroscope, such as a three-axis gyroscope, or may be acombination accelerometer/gyroscope sensor. In still other embodiments,the position sensor(s) 10 may be another type of inertial sensor, suchas a combination accelerometer and/or gyroscope further including amagnetometer. In still other embodiments, the position sensor 10 may bea sensor capable of acting as an accelerometer and a gyroscope.

In FIG. 2A, the patient 4 is depicted in a supine position 12, which mayform one exemplary position class. FIG. 2B depicts the patient 4 in aright horizontal position 13, and FIG. 2C depicts the patient 4 in aleft horizontal position 14. FIG. 2D depicts the patient 4 in a proneposition 15. Each of the depicted positions 12-15 may exemplify apredefined position class for which model waveform parameters areestablished and stored for the patient 4. Accordingly, the positionanalysis module 52 may receive position input from the position sensor10 and classify the patient position into one of the supine position 12,the right side position 13, the left side position 14, or the proneposition 15. The position class determined by the position analysismodule 52 may then be used by the waveform analysis module 54 to selectthe appropriate model waveform parameters for use in assessing thecardiac status of the patient 4.

FIGS. 3A and 3B depict an embodiment of a system 1 having a predefinedset of position classes 24 that include a set of horizontal positionsdepicted in FIG. 3A and a set of vertical positions depicted in FIG. 3B.The horizontal positions in the predefined set of position classes 24include six position classes between the right side horizontal position13 and the left side horizontal position 14. A seventh position class isdefined as the prone position class 15. Specifically, a position classis defined at every 30° increment between the right side horizontalposition class 13 labeled as 90°, the supine position class 12 labeledas 0°, and the left side horizontal position class 14 labeled as −90°.

In FIG. 3B, the exemplary embodiment of vertical position classesincluded in the predefined set of position classes 24 include a verticalposition 17, where the patient's torso is in an upright position at ornear 90° from horizontal. As explained below, the system may or may notbe configured to differentiate between standing and sitting verticalpositions, which may depend on the sensor configuration. The exemplarypredefined set of position classes 24 further includes a reclinedposition 18 where the patient's torso is at a 45° angle between thevertical position 17 and a fully horizontal position where the patientis laying down, which could take the form of any of the positionsdepicted in FIG. 3A. In other embodiments, a greater or lesser number ofpredefined position classes may be included in the predefined set ofposition classes 24. Further, the positions may be divided in equalincrements, like the depicted embodiment, or may be defined on anotherbasis. For example, the predefined set of position classes 24 mayinclude common positions, such as positions most preferred by patientsor common hospital bed settings. Alternatively or additionally, thepredefined set of position classes 24 may include expected positions forthe patient, such as based on the patient medical history and/or thepatient condition. For example, certain positions may be advised orpreferred for patients recovering from particular procedures.

The position class may be selected from a predefined set of positionclasses. In various embodiments, the predefined set of position classesmay include any number of positions. In one embodiment, the predefinedset of position classes may be those position classes 12-15 exemplifiedin FIGS. 2A-2D. In other embodiments, more or less position classes maybe defined between the right side horizontal and the left sidehorizontal. The position classes may also include various verticallydifferentiating classes, such a vertical seated position, a standingposition, and/or the position classes exemplified in FIG. 3B. Inembodiments differentiating between a vertical seated position and avertical standing position, it may be beneficial to use more than oneposition sensor 10, with at least one position sensor placed on thepatient's torso and another placed on at least one of the patient'slegs, such as on their thigh. FIG. 3B depicts an example arrangement oftwo position sensors 10 a and 10 b, where position sensor 10 a is placedon the patient's torso and position sensor 10 b is placed on one of thepatient's thighs. Thus, when the patient is in a seated position theposition sensor 10 a may detect a vertical, upright position while theposition sensor 10 b detects a horizontal position. When the patient isin a standing position, both position sensors 10 a and 10 b detect avertical position, then appropriate model waveform parameters for thestanding position class can be used. Furthermore, the position sensorarrangement may be utilized to detect when the patient is walking, forexample, by detecting certain continuous motion patterns in the motionsensors 10 a, 10 b, especially in the motion sensor 10 b on thepatient's legs. Then, the model waveform parameters for the walkingposition class could then be used.

The position analysis module 52 determines the position class based onthe position input 30, which is the output of the position sensor(s) 10.For example, the position input 30 may be classified into one of thepredefined set of position classes 24 depicted in FIGS. 3A and 3B byselecting the position class closest to the position input 30 valuedetected by the position sensor 10. To provide an explanatory example,if the position input 30 from the position sensor 10 is horizontal at10° (toward the right horizontal position), then the position analysismodule 52 may classify the position input as being in the supineposition class 12 because the supine position class 12 is the closest tothe measured position input. In another embodiment, when the patientposition falls between two adjacent position classes, the model waveformparameters for each of the two surrounding position classes may beinterpolated to create model waveform parameters that are a hybrid ofthe two surrounding models and better represents the patient's position.

Model waveform parameters may be established for each position class inthe predefined set of position classes 24, or at least each positionclass that the patient enters into for a sufficient period of time suchthat the model waveform parameters can be established. The modelwaveform parameters are data establishing a baseline or a normal for thepatient at a given position or position class. For example, the modelwaveform parameters may include a model waveform against which thepatient's cardiac waveform data 32 recorded by the ECG monitor 8 can becompared to detect changes in the patient's cardiac status. The modelwaveform parameters may include an average waveform established byaveraging the waveforms classified as belong to that same positionclass—e.g., a “normal beat” for a certain patient position. Theseaverage waveforms for each position class can be gradually updatedaccording to new data recorded in that position class. In certainembodiments, the model waveform parameters may be or may further includewaveform parameters computed from the average waveform. In still otherembodiments, the model waveform parameters may include a “snippet” of arecorded ECG waveform, which may be filtered to remove noise and/orprocessed to remove the baseline. Alternatively or additionally, themodel waveform parameters may include amplitudes and timing data of theQRS complex and/or the ST-T complex that is normal for that patientgiven a particular electrode arrangement. If electrode placement ischanged, the model waveform parameters may need to be re-established.

In one embodiment, the model waveform parameters are established foreach position where the patient 4 remains for a sufficient amount oftime for establishment of the model waveform parameters. In such anembodiment, when the patient settles in a new position, the waveformanalysis module 54 may determine whether model waveform parameters havebeen established for the determined position class. If not, the waveformanalysis module 54 may establish the model waveform parameters for thatposition class. The model waveform parameters may be established anew,or may be created by adapting or copying model waveform parameters fromanother position class, such as the closest position class for whichmodel waveform parameters are established. For example, referring to theexample of FIG. 3A, if the position class is determined to be 30°, butno model waveform parameters are available for that position class, thewaveform analysis module 54 may adapt the model waveform parametersestablished for the supine position class 12, utilizing the new cardiacwaveform data 32 being recorded from the patient 4 at the 30° position.For example, differences may be identified between the model waveformparameters for the supine position class 12 and the cardiac waveformdata 32 being recorded from the patient at 30°, and the model waveformparameters for the position class at 30° will be configured to reflectand include those differences.

In another embodiment, the model waveform parameters may be establishedfor each position class at the outset, such as by having the patient gothrough a particular series of positions and recording cardiac waveformdata at each position in order to establish model waveform parameters ateach position.

In order to avoid the problem of false alarms when a patient enters anew position for which model waveform parameters have not beenestablished, alarm conditions may be adjusted. Thus, the system 1 may beconfigured to conduct an assessment immediately upon a position changeby the patient to determine whether appropriate model waveformparameters are available for the position class associated with thepatient's new position. If not, the alarm condition thresholds may beadjusted, such as by raising the threshold for an alarm condition untilthe model waveform parameters can be established for that positionclass. Thereby, the system 1 can avoid producing false alarms due tochanges in waveforms as a result of a position change, rather than as aresult of an actual change in the patient's cardiac status. For example,the alarm thresholds may be adjusted to accommodate certain expectedchanges in ECG waveforms that may be associated with position changes.Alternatively, the alarm function may be suspended altogether until thenew model waveform parameters are established for the position class.

FIG. 4 provides another system diagram of an exemplary embodiment of thecomputer system 200 for the system 1 for monitoring cardiac function ofa patient 4 and which has a position analysis module 52 and a waveformanalysis module 54 that operate as described herein. The computingsystem 200 generally includes a processor 206, storage system 204,software 202, communication interface 208 and a user interface 210. Theprocessor 206 loads and executes software 202 from the storage system204, including the position analysis module 52, and the waveformanalysis module 54, which are applications within the software 202. Eachof the modules 52 and 54 include computer-readable instructions that,when executed by the computing system 200 (including on the processor206), direct the processor 206 to operate as described in herein infurther detail, including to execute one or more of the steps described.

Although the computing system 200 as depicted in FIG. 4 includes onesoftware element 202 encapsulating one position analysis module 52 andone waveform analysis module 54, it should be understood that one ormore software elements having one or more modules may provide the sameoperation. Thus, the functions described herein as being performed bythe respective modules 52 and 54 may be directed and executed by asingle, combined module, or by many separate modules. Similarly, whiledescription as provided herein refers to a computing system 200 and aprocessor 206, it is to be recognized that implementations of suchsystems can be performed using one or more processors, which may becommunicatively connected, and such implementations are considered to bewithin the scope of the description. For example, the processor 206 mayencompass a distributed processing system, such as in a cloud computingenvironment and system.

The processor 206 may comprise a microprocessor and other circuitry thatretrieves and executes software 202 from storage system 204. Processor206 can be implemented within a single processing device, but can alsobe distributed across multiple processing devices or sub-systems thatcooperate in executing program instructions. Examples of processors 206include general purpose central processing units, application-specificprocessors, and logic devices, as well as any other type of processingdevice, combinations of processing devices, or variations thereof.

The storage system 204, which includes the ECG database 20, can compriseany storage media, or group of storage media, readable by processingsystem 206 and/or capable of storing software 202. The storage system204 can include volatile and non-volatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer-readable instructions, data structures,program modules, or other data. Storage system 204 can be implemented asa single storage device but may also be implemented across multiplestorage devices or sub-systems. For example, the software 202 may bestored on a separate storage device than the ECG database 20. Likewise,ECG database 20 can be stored, distributed, and/or implemented acrossone or more storage media or group of storage medias. Similarly, ECGdatabase 20 may encompass multiple different sub-databases at differentstorage locations and/or containing different information which may bestored in different formats. By way of example, the ECG database 20 mayencompass a MUSE ECG management system housing waveform data. Storagesystem 204 can further include additional elements, such a controllercapable, of communicating with the processor system 206.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto storage the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. Likewise, the storagemedia may be housed locally with the processing system 206, or may bedistributed in one or more servers, which may be at multiple locationsand networked, such as in cloud computing applications and systems. Insome implementations, the storage media can be a non-transitory storagemedia. In some implementations, at least a portion of the storage mediamay be transitory.

The communication interface 208 is configured to communicate with theECG monitor 8 to receive the cardiac waveform data 52 for the patient,and to receive one position input 30from the one or more positionsensors 10. The user interface 210 may configured to receive input froma clinician, for example, and to output the cardiac status 38. The userinterface 210 may also be configured to provide notification of an alarmcondition 40. User interface 210 may include a mouse, a keyboard, avoice input device, a touch input device for receiving a gesture from auser, a motion input device for detecting non-touch gestures and othermotions by a user, and other comparable input devices and associatedprocessing elements capable of receiving user input from a user, such asa clinician. Output devices such as a video display or graphical displaycan display an interface further associated with embodiments of thesystem and method as disclosed herein and may display a visual depictionof the cardiac status 38 and/or the alarm condition notification 40.Speakers, printers, haptic devices and other types of output devices mayalso be included in the user interface 210, such as to provide anauditory notification of an alarm condition notification 40.

FIG. 5 depicts one embodiment of a method 100 of monitoring cardiacfunction of a patient. Position input is received at step 102, such asfrom one or more position sensors 10. The position class is determinedbased on the position input at step 106. At step 110, cardiac waveformdata is received, such as directly or indirectly from an ECG monitor 8.At step 114, the cardiac waveform data is compared to model waveformparameters for the position class determined at step 106.

FIG. 6 depicts another embodiment of a method 100 of monitoring cardiacfunction of a patient. After position input is received from positionsensors at step 102, the position analysis module 52 determines whetherthe position input is stable at step 104. For example, it may determinewhether the position input received sequential outputs from the one ormore position sensors are within a threshold value of one another, suchas a predetermined number of inputs that fall within the same positionclass or within a predetermined angular range of one another. In anotherembodiment where multiple position sensors are utilized, the positioninput may be determined to be stable when the outputs of the positionsensors are within a threshold range of one another. If the positioninput is not stable, then the position analysis module 52 may return tostep 102 and monitor the position input until stability is reached. Oncethe position input is stable, the position analysis module 52 determinesposition class at step 106 based on the one or more position inputs. Theposition analysis module 52 may then output position class to thewaveform analysis module 54. However, as is described above, in otherembodiments, the method steps may all be executed by a single softwaremodule that determines position class and cardiac status.

At step 110, the waveform analysis module 54 receives the cardiacwaveform data. It then determines at step 112 whether model waveformparameters are established for the position class determined at step106. If model waveform parameters are available, then the waveformanalysis module 54 compares the cardiac waveform data to the modelwaveform parameters at step 114, such as by comparing it to the averagewaveform established for the position class as is described above. Thecardiac status is then determined at step 116 based on the results ofthe comparison. The waveform analysis module 54may then output thecardiac status, such as to a clinician and/or to the ECG database 20.Additionally, as is described above, the waveform analysis module 54 mayalso assess whether alarm thresholds have been met.

Returning to step 112, if model waveform parameters have not beenestablished for the position class, then the alarm condition parametersare adjusted at step 117. At step 118, the waveform analysis module 54finds the closest position class for which model waveform parametershave been established and utilizes those parameters as a starting pointfor establishing the new model waveform parameters at step 120. The newmodel waveform parameters may be established based on the model waveformparameters for the closest position class and the cardiac waveform datareceived at step 110, such as by adjusting the model waveform parametersfor the closest position class based on the differences between it andthe cardiac waveform data. At step 122, the new model waveformparameters are stored in association with the position class, such as inthe ECG database 20. Assuming that the patient position has not changed,the waveform analysis module 54 may then assess the cardiac status ofthe patient using the newly-established model waveform parameters, asdescribed in steps 110 through 116 and according to the methodsdisclosed herein.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

I claim:
 1. A method of monitoring cardiac function of a patient, themethod comprising: receiving at a processor a position input from aposition sensor attached to the patient; classifying the position inputinto a position class; based on the position class, accessing modelcardiac waveform parameters associated with the position class, whereinthe model cardiac wave form parameters establish a baseline when thepatient's position is in the position class; receiving cardiac waveformdata for the patient; and comparing the cardiac waveform data to themodel cardiac waveform parameters associated with the position class. 2.The method of claim 1, wherein the position class is one of a predefinedset of position classes.
 3. The method of claim 2, wherein thepredefined set of position classes includes at least a right sidehorizontal position, a left side horizontal position, a supine position,and a vertical position.
 4. The method of claim 3, wherein the positioninput is classified into the closest one of the predefined set ofposition classes.
 5. The method of claim 2, further comprising storing aset of model cardiac waveform parameters for each position class in thepredefined set of position classes, wherein the model cardiac waveformparameters are based on cardiac waveform data recorded from the patient.6. The method of claim 1, further comprising determining that theposition input is stable prior to classifying the position input into aposition class.
 7. The method of claim 1, further comprising storing newmodel waveform parameters in association with the position class basedon cardiac waveform data recorded from the patient.
 8. The method ofclaim 7, further comprising determining a closest position class forwhich model waveform parameters are established, and establishing thenew model waveform parameters for the position class based on the modelwaveform parameters for the closest position class.
 9. The method ofclaim 7, further comprising adjusting one or more alarm conditions untilthe new model waveform parameters are established for the positionclass.
 10. A system for monitoring cardiac function of a patient, thesystem comprising: an electrocardiograph monitor configured to recordcardiac waveform data from the patient; at least one position sensorconfigured to sense a position of the patient and produce a positioninput; a processor; a position analysis module executable on theprocessor to: receive the position input; determine a position classbased on the position input, wherein the position class is one of apredefined set of position classes; output the position class; awaveform analysis module executable on the processor to: receive thecardiac waveform data; identify appropriate model cardiac waveformparameters based on the position class wherein the model cardiac waveform parameters establish a baseline when the patient's position is inthe position class; compare the cardiac waveform data to the modelcardiac waveform parameters for the position class to determine acardiac status of the patient; and output the cardiac status.
 11. Thesystem of claim 10, wherein the predefined set of position classesincludes at least a right side horizontal position, a left sidehorizontal position, a supine position, and a vertical position.
 12. Thesystem of claim 11, wherein the position input is determined as theclosest one of the predefined set of position classes.
 13. The system ofclaim 12, wherein the position analysis module is further executable onthe processor to determine that the position input is stable prior toclassifying the position input into a position class.
 14. The system ofclaim 11, wherein the waveform analysis module further comprises a setof model cardiac waveform parameters for each position class in thepredefined set of position classes; and wherein identifying appropriatemodel cardiac waveform parameters includes selecting a set of modelcardiac waveform parameters stored in association with the positionclass.
 15. The system of claim 14, wherein the position sensor includesan accelerometer attachable to a torso of the patient, and wherein thesystem further comprises a second accelerometer attachable to thepatient and configured to provide a second position input, wherein theposition analysis module is further executable on the processor toreceive the second position input and determine the position class basedon the position input and the second position input.
 16. The system ofclaim 10, wherein the waveform analysis module is further executable onthe processor to establish new model cardiac waveform parameters for theposition class based on cardiac waveform data recorded from the patient.17. The system of claim 16, wherein the waveform analysis module isfurther executable on the processor to adjust one or more alarmconditions until the new model cardiac waveform parameters areestablished for the position class.
 18. A non-transitorycomputer-readable medium having computer-executable instructions storedthereon, wherein the instructions include steps comprising: receiving aposition input from a position sensor attached to the patient;classifying the position input into a position class, wherein theposition class is one of a predefined set of position classes;identifying appropriate model cardiac waveform parameters based on theposition class wherein the model cardiac wave form parameters establisha baseline when the patient's position is in the position class;receiving cardiac waveform data for the patient; and comparing thecardiac waveform data to model cardiac waveform parameters for theposition class.
 19. The non-transitory computer-readable medium of claim18, wherein the instructions include the steps further comprisingaccessing a database comprising a set of model cardiac waveformparameters for each position class in the predefined set of positionclasses, and wherein identifying appropriate model cardiac waveformparameters includes selecting a set of model cardiac waveform parametersfrom the database based the position class.
 20. The non-transitorycomputer-readable medium of claim 18, wherein the instructions includethe steps further comprising establishing new model cardiac waveformparameters for the position class based on cardiac waveform datarecorded from the patient.